Indoor Top-k Keyword-aware Routing Query

Zijin Feng, Tiantian Liu, HUAN LI, Hua Lu, Lidan Shou, Jianliang Xu

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

1 Citationer (Scopus)
50 Downloads (Pure)

Abstrakt

People have many activities indoors and there is an increasing demand of keyword-aware route planning for indoor venues. In this paper, we study the indoor top-k keyword-aware routing query (IKRQ). Given two indoor points s and t, an IKRQ returns k s-to-t routes that do not exceed a given distance constraint but have optimal ranking scores integrating keyword relevance and spatial distance. It is challenging to efficiently compute the ranking scores and find the best yet diverse routes in a large indoor space with complex topology. We propose prime routes to diversify top-k routes, devise mapping structures to organize indoor keywords and compute route keyword relevances, and derive pruning rules to reduce search space in routing. With these techniques, we design two search algorithms with different routing expansions. Experiments on synthetic and real data demonstrate the efficiency of our proposals.

OriginalsprogEngelsk
TitelThe 36th IEEE International Conference on Data Engineering (ICDE 2020)
Antal sider12
ForlagIEEE
Publikationsdatoapr. 2020
Sider1213-1224
Artikelnummer9101652
ISBN (Trykt)978-1-7281-2904-4
ISBN (Elektronisk)9781728129037
DOI
StatusUdgivet - apr. 2020
Begivenhed36th IEEE International Conference on Data Engineering - Dallas, USA
Varighed: 20 apr. 202024 apr. 2020
https://www.utdallas.edu/icde/

Konference

Konference36th IEEE International Conference on Data Engineering
LandUSA
ByDallas
Periode20/04/202024/04/2020
Internetadresse

Fingeraftryk Dyk ned i forskningsemnerne om 'Indoor Top-k Keyword-aware Routing Query'. Sammen danner de et unikt fingeraftryk.

  • Citationsformater

    Feng, Z., Liu, T., LI, HUAN., Lu, H., Shou, L., & Xu, J. (2020). Indoor Top-k Keyword-aware Routing Query. I The 36th IEEE International Conference on Data Engineering (ICDE 2020) (s. 1213-1224). [9101652] IEEE. https://doi.org/10.1109/ICDE48307.2020.00109